Autonomous vehicles, also referred to as self-driving cars, navigate autonomously through an environment with minimal or no human input. To navigate autonomously, a vehicle precisely determines a location within an environment so that various obstacles can be avoided and to ensure that the vehicle remains on the roadway. In general, autonomous vehicles use various sensors including, for example, LIDAR sensors, radar sensors, cameras, and other sensors to help the vehicle detect and identify obstacles and other features in the environment. Additionally, the vehicle may also use the sensors to precisely locate the vehicle within the environment. Thus, by way of example, the vehicle can use point clouds produced by a LIDAR sensor to localize the vehicle within the environment. However, localizing the vehicle in this manner generally uses a prior mapping of the environment so that the vehicle can compare inputs from the LIDAR against features of the map to provide a location.
As a result, the vehicle is preloaded with a map of the environment. Additionally, various portions of the map are labeled so that the vehicle can simply discern different features and items within the map from one another. However, as environments change over time from obstacles being modified and/or from other objects moving about, the map can become inaccurate. Moreover, subsequent data acquisitions about the environment generally result in redundant labeling of elements that have not changed. Accordingly, updating and labeling map data can be a computationally intensive task that is complicated through processing redundant data.